Exploration of Deep Belief Networks for Vowel-like regions detection

This work explores Deep Belief Networks (DBN) for the task of detecting Vowel-like regions (VLRs). Vowels and semivowels are considered as VLRs. By using vocal tract features at the input layer of DBN, we extract an evidence for VLRs by transforming the vocal tract features through multiple non-linear hidden layers. The linear classifier is used to predict the class of evidence, i.e.,whether it is VLR or not. The DBN method is then combined with excitation source (ES) based method for VLRs detection. Even though DBN method provides comparable performance with the existing methods, the combination provides improved performance confirming the different way of modeling VLR information in the DBN.